By Marlene Cimons, National Science Foundation
The questions have consumed educational researchers for years: how do children learn, and what are the best ways to teach them? Should the emphasis be on practicing basic skills, or on encouraging deep understanding and conceptual thinking, or both?
“Considering there are many different kinds of knowledge we want our children to acquire, what are the best ways to get them there?” says Kenneth R. Koedinger, professor of human-computer interaction and psychology at Carnegie Mellon University. “We are looking to develop concepts about what is the right way to teach and to learn.”
Scientists at the Pittsburgh Science of Learning Center, a collaboration between Carnegie Mellon University and the University of Pittsburgh, and its research facility, LearnLab, are seeking these answers through a series of studies using classrooms in more than 50 schools and colleges in the Pittsburgh area and around the country.
LearnLab addresses a longtime dilemma for educational researchers, the dichotomy between experiments conducted in the artificial setting of a laboratory, which can produce results not always transferrable to schools, and those conducted in classrooms, which tend to be less rigorously controlled.
LearnLab’s use of advanced technologies enables the researchers to design experiments that combine the realism of classroom field studies with the rigor of those that take place in the lab. They are studying teaching and learning in math (algebra, geometry), science (physics, chemistry) and language (Chinese and English as a second language) courses, using computer programs as a major component of instructional programs.
“In our modern technology world, it’s easier to take our insights and bring them into practice,” says Koedinger, director of the center. For example, “we built a computer based algebra course that 600,000 students now use as part of their algebra class. Those kinds of educational technologies are a platform for doing research in real settings and real course to make it better for kids.”
With such technology, “we can get at the details of what works and what doesn’t,” he adds. “The technologies enable us to get more data to figure out what works. As soon as we figure it out, it’s part of the course. So we are using technology in the classroom both as a teaching tool and a research tool.”
The center, established in 2004, is a National Science Foundation (NSF) Science of Learning Center, funded by NSF with $5 million annually. LearnLab draws upon the two Pittsburgh universities’ expertise in cognitive and developmental psychology, human-computer interaction and intelligent tutoring systems, matching learning and language technologies. Researchers at Carnegie Mellon, for example, designed Cognitive Tutor®, the computer-based tutoring program Koedinger describes above, that includes a comprehensive secondary mathematics curriculum now in use in 1,700 schools nationwide.
The researchers already have reached some intriguing conclusions.
Every student, for example, knows that homework is a fact of life, albeit one whose value has been the subject of endless debate. LearnLab researchers have discovered ways to make that nightly task more effective.
In math, for example, traditionally, students receive a list of math problems to solve. But this approach “gives novice learners too little support in constructing new knowledge,” Koedinger says. “It’s not as effective as replacing about half of those problems with example solutions. Rather than guessing their way through problems, these worked-out examples allow students to focus on grasping the thinking needed so they can solve future problems on their own.”
Thus, “if every other problem contains a step-by-step solution, students learn more robust skills,” he adds. “Even better is adaptive computer-based practice that adjusts to individual students, providing more worked-out solution steps initially, but then gradually challenging a student with more problems as he or she increases in understanding and skill.”
But Koedinger is quick to point out that using more worked examples is not the answer for all learning goals. “They are best for skills, but pure practice is better for facts,” he says. “For deeper concepts and principles, more emphasis on providing explanations is important, but should these explanations simply be given to students?”
Koedinger says no. “LearnLab researchers have shown that students learn math and science principles more effectively when the students themselves must explain them, instead of having their teachers do so,” he says. “Here’s a problem, here’s a solution, you tell me how we get there. So there is more emphasis on the thinking, rather than on the doing. “
To further illustrate that “one size fits all” does not work in education, LearnLab researchers found that prompting students to explain is not always an ideal practice. It works very well for math and science principles, but not so for implicit skills in language, such as selecting the right article, “the” or “a,” for example, in learning English as a second language.
“Most of us who know English don’t know the rules in a verbal form,” Koedinger says. “A lot of what we learn, we learn through experience, imitation and practice. It’s implicit. Your brain learns the grammar, but doesn’t learn how to talk about it. Interestingly, we have shown that this kind of learning is important not only for language, but also for success in math and science.”
The debates over education--how best to teach and learn--are too often over-simplified without the nuances needed for true progress, he says.
“We continually get into these two-sided education wars, basics vs. understanding, teacher-centered vs. student-centered, guidance vs. discovery,” he says. “Our center has explored numerous instructional approaches, which can be combined in many ways, and have consistently found that what works best changes depending on the learning goals.
“There are not two options, but a multitude of instructional choices for the science of learning to work out, “he adds. “We are trying to figure out which instruction is best for which type of knowledge, so instead of having blanket policies, we pick the right instructional strategies for the knowledge objectives.”
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